Analytics Data Integration: In-App Setup Assistance, Data Sync Schedule Reminders, More
Get in-app help when you set up Analytics. Get a reminder when a
data job includes potentially stale data from unscheduled data sync. Convert dataset
field types and date formats with new recipe transformations.
Data Sync Is Enabled by Default in New Analytics Orgs
Starting in Winter ’20, Data Sync is enabled by default when you turn on Analytics for your org. Data Sync speeds up your dataflows by loading your Salesforce and external data to Analytics before the dataflows runs. Data Sync also enables Analytics connectors that enrich Analytics with data stored outside your org. If you turned on Analytics for your org before Winter ’20, you can manually enable Data Sync.
Data Sync Schedule Reminders Help You Keep Your Data Current
The Monitor page now displays a message when an SFDC_Local connected object is used in a job but the connection’s data sync isn’t scheduled. You wouldn’t rely on last week’s movie times to decide what you’ll see tomorrow, and the same is true for data in Analytics. Lenses and dashboards are most valuable when a dataset has up-to-date data. This message keeps you aware of any job using potentially stale local org data from the SFDC_Local connection.
Sync More Data Through Snowflake Computing and Amazon S3 Connections
Bring your remote data into Analytics for richer analysis, smarter insights, more accurate forecasting, and more. Now you can sync more data per connected Snowflake Computing and Amazon S3 object, up to 100 million rows or 50 GB, whichever limit is reached first. We’re applying the increased data sync limit to Snowflake Computing and Amazon S3 connections on a rolling basis during the Winter ’20 release.
Capture the Latest Data Faster
Schedule data sync, dataflow, and recipe jobs to run on shorter intervals. You can now schedule these jobs to run every 15, 20, 30, or 60 minutes.
Identify Users Not Covered by Sharing Inheritance
Are some users complaining that they can’t see data from a particular dataset? To identify users who don’t have access to the dataset based on inherited sharing rules, run the Sharing Inheritance Coverage report. Use this report to determine if you must add a security predicate to grant more users access to the dataset. For example, add a predicate to grant a user access to the Opportunities dataset when the user isn’ t covered by sharing rules for the Opportunity object.
Recipe Editor Is Renamed Data Prep
It’s your key data preparation tool, so let’s call it what it is—“data prep,” short for data preparation. Use data prep to create recipes that clean, combine, transform, and filter data loaded into datasets.
Complete Your Data by Predicting Missing Values
Missing data can throw off your query results. To minimize the impact, fill in missing values in a dimension column with the Predict Missing Values transformation in a recipe. Analytics intelligently predicts values based on values in other strongly correlated columns in your data.
Convert Columns to Dimensions and Dates
The field type of a dataset column determines how you can query that field’s data. For example, you can filter and group by a dimension or date, or perform math calculations on a measure. When you create a dataset, Analytics sometimes tags a column with the wrong field type. If needed, use the To Dimension recipe transformation to convert measure columns to dimensions, and the To Date transformation to convert dimension columns to dates.
Standardize Date Formats in Your Datasets
If a dimension column in a dataset contains dates in different formats, use the Format Dates recipe transformation to standardize the format for all values in the column. A consistent format enables you to correctly filter and group records by date, including filtering by date component, such as day or month. It also ensures that you can successfully convert the field type from dimension to date.
Get the Right Data with More Flexible Recipe Filters
You can now filter on measure and date columns, not just dimensions. We added new operators, such as less than and greater than, for dimension filters. And you can now manually enter your own filter values, instead of selecting from existing values. For example, you can filter on a string value that you expect to show up in your data.
Manage Recipes and Dataflows More Easily
You can now search for a recipe by name, see who last modified it, and view its next scheduled run—all in the Recipes tab. You can also search for dataflows by name in the Dataflows tab.
Analytics Applies Security to Datasets Exported to Einstein Discovery
When exporting a dataset to Einstein Discovery using the export dataflow transformation, Analytics now applies the security predicate. Analytics applies the security predicate based on the user specified in the transformation parameters, omitting records that the user doesn’t have access to. Previously, the transformation exported all records, regardless of the security predicate.
Make Datasets More Secure with Longer Security Predicates
Some security predicates, like territory management with multiple territory levels, require more security conditions. To enable you to create finer row-level security on a dataset, a security predicate now supports up to 5,000 characters, instead of 1,000.